2023
doi: 10.12720/jait.14.3.463-471
Intelligent Fault Detection Based on Reinforcement Learning Technique on Distribution Networks
Tlotlollo S. Hlalele1,2,*
, ,
- 1Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa
- 2Department of Electrical Engineering, University of South Africa, Johannesburg, South Africa
- Manuscript receivedAugust 1, 2022
- revisedAugust 25, 2022
- acceptedSeptember 19, 2022
- publishedMay 15, 2023
Abstract
The incorporation of distributed energy resources in the distribution networks changes the fault current level and makes the fault detection be more complex. There are several challenges brought by these heterogenous energy systems including power quality, voltage stability, reliability and protection. In this paper, a fault detection based on reinforcement learning approach is proposed. The heart of this approach is a Q learning approach which uses a non-adaptive multi-agent reinforcement learning algorithm to detect and identify nonlinear system faults, and the algorithm learns the policy by telling an agent what actions to take under what circumstances. Moreover, the Discrete Wavelet Transform (DWT) is utilized to extract coefficient values from the captured one-fourth cycle of the three-phase current signal during fault which occurs during the transient stage. The simulations and signal analysis for different faults are used to validate the proposed fault detection method in MATLAB environment. The simulation results show that different types of faults such as CA, AB, ABC and ABCG can be detected and the best correlation coefficient achieved is 0.87851.
Keywords
- fault detection
- distributed energy resources
- reinforcement learning
How to Cite
Tlotlollo S. Hlalele, Yanxia Sun, and Zenghui Wang, "Intelligent Fault Detection Based on Reinforcement Learning Technique on Distribution Networks," Journal of Advances in Information Technology, vol. 14, no. 3, pp. 463-471, 2023. https://doi.org/10.12720/jait.14.3.463-471
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
